We’re trying to segment and count EdU-stained cell nuclei in a stack of images and we’re not quite happy with the results to date. We’re convinced they can be improved, but don’t know how to go about it. After watching many videos (there’s great material online!) and reading posts in this forum, we’ve set the following pipeline:
- Background subtract
- Trainable Weka Segmentation 3D
- Convert results to 8-bit grayscale, fill holes and apply Gaussian blur 3D.
- 3D watershed split.
After segmenting, we tried several filters. Erosion is definitely not helpful in our case. I’m not sure Gaussian Blur is helping much since we must use very low sigma values (about 1). The issue is that we must keep the shape of our blobs with several cells stuck together for the watershed to be able to split them. We tried retraining the algorithm in the Weka segmentation several times, but don’t know how to further improve the outcome. We have also tried playing around with the parameters for the watershed split. For example, using the probability map from Weka Segmentation as seeds doesn’t seem to help. Our best results are with automatic seeds and a radius of 25 px (that’s approx the size of a cell).
It’s our first time trying to segment cells. We would really appreciate tips from more experienced users on how to improve our results. Specifically, how can we get the “big blobs” to split into separate cells?
Note that this post is related to our previous query, but I’m opening a new post because we solved the problem with the “floating pixels” and our question now is not exclusively related to the Weka segmentation any more: